Received: 13 April 2020    Accepted: 11 August 2020 DOI: 10.1111/gcb.15336


Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index Daniel T. C. Cox

 | Ilya M. D. Maclean

Environment and Sustainability Institute, University of Exeter, Penryn, UK Correspondence Daniel T. C. Cox, Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall TR10 9FE, UK. Email: [email protected] Funding information Natural Environment Research Council, Grant/Award Number: NE/P01156X/1 and NE/P01229/1

 | Alexandra S. Gardner

 | Kevin J. Gaston

Abstract The impacts of the changing climate on the biological world vary across latitudes, habitats and spatial scales. By contrast, the time of day at which these changes are occurring has received relatively little attention. As biologically significant organismal activities often occur at particular times of day, any asymmetry in the rate of change between the daytime and night-time will skew the climatic pressures placed on them, and this could have profound impacts on the natural world. Here we determine global spatial variation in the difference in the mean annual rate at which near-surface daytime maximum and night-time minimum temperatures and mean daytime and mean night-time cloud cover, specific humidity and precipitation have changed over land. For the years 1983–2017, we derived hourly climate data and assigned each hour as occurring during daylight or darkness. In regions that showed warming asymmetry of >0.5°C (equivalent to mean surface temperature warming during the 20th century) we investigated corresponding changes in cloud cover, specific humidity and precipitation. We then examined the proportional change in leaf area index (LAI) as one potential biological response to diel warming asymmetry. We demonstrate that where night-time temperatures increased by >0.5°C more than daytime temperatures, cloud cover, specific humidity and precipitation increased. Conversely, where daytime temperatures increased by >0.5°C more than night-time temperatures, cloud cover, specific humidity and precipitation decreased. Driven primarily by increased cloud cover resulting in a dampening of daytime temperatures, over twice the area of land has experienced night-time warming by >0.25°C more than daytime warming, and has become wetter, with important consequences for plant phenology and species interactions. Conversely, greater daytime relative to night-time warming is associated with hotter, drier conditions, increasing species vulnerability to heat stress and water budgets. This was demonstrated by a divergent response of LAI to warming asymmetry. KEYWORDS

activity patterns, climate change asymmetry, daytime warming, leaf area index, night-time warming, vegetation cover, warming asymmetry, water cycle

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Global Change Biology published by John Wiley & Sons Ltd Glob. Change Biol.. 2020;00:1–13. 


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1 |  I NTRO D U C TI O N

COX et al.

Rising temperatures are associated with increased evapotranspiration that has been attributed to global trends of increasing cloud cover

Human activities are altering the planet's climate, with biologi-

and near-surface humidity and precipitation (e.g. Huntington, 2006;

cally meaningful changes taking place from the macro- (IPCC,

Ohmura & Wild,  2002), further influencing daily temperature

2013) to the microscale (e.g. Maclean, Suggitt, Wilson, Duffy, &

ranges (e.g. Dai et al., 1999; Du, Wu, Jin, Zong, & Meng, 2013; Zhou

Bennie,  2017; Suggitt et  al.,  2018). Anthropogenic increases in

et al.,  2015). Because a warmer atmosphere is able to hold more

atmospheric CO2 and other greenhouse gases have resulted in

moisture, one might predict that asymmetric warming may impact

a strong global trend of increasing maximum and minimum tem-

cloud cover, specific humidity and precipitation differently at differ-

peratures (IPCC, 2013), which in turn has driven an intensifica-

ent times of the day. Consequently, in regions where the night-time

tion of the hydrological cycle (e.g. Huntington,  2006; Ohmura

is warming faster than the daytime we may expect to see greater rel-

& Wild,  2002). The rates and directions in which these changes

ative increases in night-time specific humidity than daytime specific

are occurring have been shown to vary with land use and land

humidity owing to the temperature-dependent limits on how much

cover change (e.g. Pielke, 2005), across elevations (e.g. Rangwala,

water vapour a given volume of air can hold (Gaffen & Ross, 1999;

Sinsky, & Miller,  2013), ecoregions (e.g. Beaumont et al., 2011;

Wang & Gaffen, 2001). Conversely, relatively higher temperatures

Zhou, Chen, & Dai, 2015) and latitudes (e.g. Deutsch et al., 2008),

during the daytime potentially enable more moisture to be held in the

and the associated impacts of these changes on the natural

atmosphere, which can lead to increased levels of specific humidity

world have been diverse (e.g. Maclean & Wilson,  2011; Walther

and precipitation during the daytime.

et al., 2002). By contrast with the rich literature examining spa-

As biological activities and processes often occur at partic-

tial variation in climate change, that exploring temporal varia-

ular times of day, diel asymmetries in warming, cloud cover, hu-

tion focuses principally on changes to daily, monthly, or annual

midity or precipitation are likely to have diverse and profound

means. Variation in the rates at which these changes are occur-

consequences for the natural world. In plants, for example, rising

ring across the diel (daily) cycle has received surprisingly little at-

minimum night-time temperatures are expected to affect carbon

tention. However, if the climate is not changing at a consistent

assimilation and consumption, because most photosynthesis oc-

rate during both the daytime and night-time, then the use of daily

curs during the daytime and is more sensitive to maximum daytime

means will lead to underestimation or overestimation of changes

temperatures, whereas respiration occurs throughout the diel cycle

across a particular dimension of the day. Given that many species

(Atkin et al., 2013) and is influenced by both daytime maximum and

show strong time partitioning in their daily biological processes

night-time minimum temperatures (Peng et  al.,  2013; Peraudeau

(e.g. plant photosynthesis; Xu, Medvigy, Powers, Becknell, &

et al., 2015; Xia et al., 2014). Night-time warming has been found to

Guan, 2016) or activity patterns (Bennie, Duffy, Inger, & Gaston,

affect vegetation growth (Alward, Detling, & Milchunas, 1999; Peng

2014), neglecting sub-daily changes in climate could have import-

et al., 2004, 2013; Xia et al., 2014), however, responses of vegetation

ant consequences for predicting the impacts of climate change on

to diel asymmetric warming differ between regions and ecosystems

global biodiversity.

(Alward et al., 1999; Beier et al., 2008; Peng et al., 2004, 2013; Wan,

There has been a global trend of night-time temperatures in-

Xia, Liu, & Niu, 2009) and large-scale responses are less clear. In the

creasing at a faster rate than daytime temperatures (Davy, Esau,

Northern Hemisphere, positive correlations have been observed

Chernokulsky, Outten, & Zilitinkevich, 2017; Easterling et al., 1997;

between daytime maximum temperatures and vegetation growth in

Karl et al., 1991; Vose, Easterling, & Gleason, 2005), which has led to

wet and cool ecosystems, but negative correlations in dry temperate

a shrinking of the diel temperature range (e.g. Vose et al., 2005). This

regions (Peng et al., 2013). However, global trends in changing veg-

diel asymmetry in warming varies spatially, with some regions also

etation cover in response to warming asymmetry are not currently

experiencing daytime temperatures increasing faster than night-time


temperatures (Donat & Alexander, 2012; Easterling et al., 1997). The

Studies to date investigating diel asymmetry in warming have

downward trend in daily temperature ranges is thought ultimately to

been hampered by incomplete spatial coverage (e.g. Easterling

be related to the upward trend in cloud cover over land (Dai, Genio,

et al., 1997; Karl et al., 1993; Vose et al., 2005) or have tended to focus

& Fung, 1997; Dai, Trenberth, & Karl, 1999; Sun, Groisman, Bradley,

on a specific hemisphere (e.g. Davy et al., 2017; Peng et al., 2013) or

& Keimig, 2000), with clouds having a strong and opposing effect on

region (e.g. Kuang & Jiao, 2016). Moreover, it is not known whether

temperature, dependent on the time of day (Sun et al., 2000). During

diel warming asymmetry is matched by diurnal asymmetry in other

the daytime, clouds attenuate shortwave radiation resulting in a low-

climate variables such as cloud cover, specific humidity or precipita-

ering of daytime temperatures, whilst at night they re-emit longwave

tion. Globally, it is currently unclear how diurnal asymmetry in the

radiation downward, warming the earth's surface (Dai et al., 1999).

changing climate varies spatially, where the extreme differences be-

Warming asymmetry has also been attributed to soil moisture

tween daytime and night-time warming are taking place, and what

(Dai et al., 1999), precipitation (Dai et al., 1997; Zhou et al., 2009),

proportion of the land's surface is undergoing different rates of

boundary layer depth (Davy et  al.,  2017) and at smaller spatial


scales, anthropogenic land forcing (e.g. Chen & Dirmeyer, 2019; Lee et al., 2011).

Here we use a 35 year global data set for near-surface temperature, cloud cover, specific humidity and precipitation to examine: (a)



COX et al.

associations between the long-term trend in mean daily cloud cover

period. Finally, to avoid giving undue weighting to pixels at high

and the difference in the rate of change between daytime maximum

latitudes for each year, we reprojected the rasters for each climate

temperatures and night-time minimum temperatures; (b) spatial vari-

variable to the Behrmann equal area projection (181 × 243 km res-

ation in the difference in the rate of change between mean daytime

olution; EASE-Grid 2.0: EPSG:6933; Brodzik, Billingsley, Haran,

and mean night-time cloud cover, specific humidity and precipita-

Raup, & Savoie, 2012).

tion; (c) the relationship between warming asymmetry and diurnal asymmetry in cloud cover, specific humidity and precipitation in regions that have experienced a greater increase of >0.5°C in the

2.1 | Daytime and night-time climate trends

daytime or the night-time; and (d) climate change asymmetry in three case study regions that display high degrees of warming asymmetry,

In each terrestrial pixel for every 24 hr cycle, we determined the

namely the Tibetan Plateau, West Africa and East Africa. Finally, (e)

maximum daytime temperature and the minimum night-time tem-

we examine one potential biological response to diel warming asym-

perature, before taking the mean across each year. We focused on

metry, change over recent decades in vegetation cover (measured

daytime maximum and night-time minimum temperature, because

through the leaf area index; LAI).

we were interested in the ecological impacts of changing extremes of daytime and night-time temperature variation that have

2 | M E TH O DS

been shown to have greater ecological impacts than temperature means (Ma, Hoffmann, & Ma, 2015). Unlike temperature, cloud cover, specific humidity and precipitation do not follow a similar

We used NCEP Reanalysis 2 climate data provided by the NOAA/

consistent daily cycle, and so we examined long-term trends in an-

OAR/ESRL PSD (https://www.esrl.noaa.gov/psd/). This is a global

nual mean daytime and annual mean night-time measures. Higher

gridded data set of modelled forecasts and hindcasts calibrated

latitudes experience 24 hr daylight and darkness and so diel asym-

against observed data and is the best long-term reanalysis data

metry is conflated with seasonal asymmetry, and we therefore

set currently available. All spatial manipulations were performed

excluded 24  hr periods that were solely day or night. To obtain

in QGIS v3.4 (QGIS Development Team, 2018) and R software for

a global average for each climate variable in each year for the day-

statistical computing v3.5.2 (R Core Team, 2018). We used the

time and again for the night-time we calculated the mean value

R packages ‘microclima’ (Maclean, Mosedale, & Bennie,  2019)

across pixels. We then built a linear model of the annual global

for data preparation and ‘raster’ (Hijmans,  2019) for raster

anomaly relative to the 35 year mean for daytime and night-time


(response) modelled first against year (0–34) and then against

We downloaded near-surface 6 hourly observations for tem-

the interaction between year and a binary factor of daytime and

perature (°C) at 2 m above ground level, cloud cover (%), specific

night-time. As a measure of the level of variation explained by the

humidity (g/m3) and the precipitation rate at the surface (mm). We

linear regression, for each model we also calculated McFadden's

used specific humidity instead of relative humidity to avoid con-

pseudo R-squared.

flation with temperature. Data were from 1983 to 2017 and re-

To understand the spatial distribution of diel warming asym-

corded at 1.904 × 1.875 degree resolution. The data set covered

metry, for each year for the daytime and again for the night-time,

from 89.5°N to 89.5°S and was projected using WGS84. A spline

we carried out linear regression across years for each pixel before

interpolation was then used to produce hourly climate estimates

multiplying the slope by 35  years to calculate the absolute level

for cloud cover and specific humidity. For temperature, a more

of warming between 1983 and 2017. To calculate the difference

sophisticated interpolation was used in which this is assumed to

in the rate of change between the daytime and the night-time,

follow a sinusoidal diurnal cycle, with the periodicity determined

we subtracted the daytime change from the night-time change.

by sunrise and sunset. Final values for temperature were then cal-

We then repeated this process for cloud cover, specific humidity

culated using the ‘hourlytemp’ function in the R package ‘micro-

and precipitation. To capture the area of land experiencing greater

clima’ where deviations from this cycle are determined by daytime

change in the daytime or the night-time, we calculated the per-

solar radiation and night-time sky emissivity. The ‘suntimes’ func-

centage of land that crossed an arbitrary asymmetry threshold,

tion provided the time of sunrise and sunset for the centroid of

namely temperature >0.25°C, cloud cover >2.5%, specific humid-

each pixel, allowing a binary raster for each hour of the day to be

ity >0.1 g/m3 and precipitation >0.1 mm. Finally, we plotted the

generated showing whether this was during the daytime or night-

daytime and night-time annual global anomalies against the long-

time (rounded to the nearest hour). The ocean exerts different

term means.

pressures on the climate compared to the land (Sutton, Suckling,

Cloud cover has a strong influence on surface solar heating and

& Hawkins,  2015), therefore for all climate variables we masked

upward longwave radiation (Dai et al., 1997; Sun et al., 2000), there-

pixels where the ocean covered the centroid of the pixel. We also

fore we examined the relationship between mean daily cloud cover

excluded small islands. For precipitation, for which estimates are

and the temperature change difference between day and night. For

provided over a 6  hr period, amounts were partitioned accord-

each pixel and year as above, we calculated the mean daily cloud

ing to whether more than half of day or night fell within the 6 hr

cover before carrying out linear regression across years for each



COX et al.

pixel to determine the level of cloud cover change between 1983

LAI values was downloaded from the National Climatic Data Center

and 2017. We then calculated Pearson's correlation between the diel

(Vermote & NOAA CDR Program, 2019). Values were then aver-

temperature change difference and (a) mean daily cloud cover, (b)

aged for each month. Missing values from regions obscured by cloud

diel cloud clover change difference, (c) diel specific humidity change

cover, or not obtained due to adverse data capture conditions were

difference, and (d) diel precipitation change difference.

imputed as follows. First, the entire globe was divided into 100,

To explore the relationship between diel warming asymme-

36° × 18° regions. Within each region mean values for each month

try and diurnal differences in the rate of change of cloud cover,

were calculated, and all grid cells values were divided by the mean

specific humidity and precipitation, for each climate variable

monthly value. Missing values were then approximated by spline

we selected all pixels that experienced: (a) night-time minimum

interpolation on the time series for each grid cell, before multiply-

warming of >0.5°C more than the daytime maximum, and (b) day-

ing by the mean monthly value to add the seasonal effect back in.

time maximum warming of >0.5°C more than the night-time mini-

This, in effect, removes the season effect before interpolation so

mum (0.5°C being the level that the mean surface air temperature

as to ensure that if missing values coincided with seasonal peaks or

warmed during the 20th century; IPCC, 2013). For the daytime

troughs in LAI, values are not over- or underestimated. For a small

and night-time we then plotted the global anomaly against the

number of locations with a high proportion of missing data, LAI val-

long-term mean.

ues could not be estimated in this way. For these locations, a land

To understand the relationship between warming asymmetry

cover type was identified using a data set from the European Space

and diel asymmetry in cloud cover, specific humidity and precipita-

Agency (Lamarche et  al.,  2017). Grid cells were then assigned the

tion at smaller spatial scales, which may be influenced by different

mean monthly value of LAI for that land cover type, estimated using

processes and pressures, we examined climate asymmetry in three

non-missing data and separately for each 36 × 18° region.

case study regions that displayed high levels of warming asymme-

Data were aggregated in annual composites of mean LAI, be-

try (e.g. Pingale, Adamowski, Jat, & Khare,  2015): (a) the Tibetan

fore being reprojected to the Behrmann equal area projection and

Plateau, a high altitude site that has experienced high levels of night-

resampled to match the resolution of the equal area climate layers

time warming and has been the focus of intense climate research;

(181 × 243 km). For each land pixel, we carried out linear regression

and two tropical regions that experienced asymmetry in, respec-

across years before calculating the total loge proportional change in

tively, the night-time (b) West Africa, and the daytime (c) East Africa.

LAI between 1983 and 2017. We then modelled the loge propor-

At each site for the daytime and the night-time we plotted the global

tional change in LAI (response) against the interaction between the

anomaly against the long-term mean.

absolute change in total annual precipitation and a binary factor of whether warming occurred more during the daytime or the nighttime. The absolute change in precipitation in each pixel was deter-

2.2 | The response of vegetation cover to warming asymmetry

mined as the linear regression of the total annual precipitation in each pixel multiplied by 35 years.

To examine a biological response to the 35  year change in warming asymmetry, we quantified relationships with the loge propor-

3 | R E S U LT S

tional change in LAI. LAI is the total one-sided leaf area per unit ground area and therefore a strong indicator of volumetric biomass

Global trends did not reveal diurnal asymmetry in the annual rate

within an ecosystem and is a critical variable scaling photosynthe-

at which daytime maximum and night-time minimum near-surface

sis, respiration and evapotranspiration (Asner, Braswell, Schimel, &

temperatures, and mean daytime and mean night-time cloud cover,

Wessman, 1998; Boussetta, Balsamo, Beljaars, Kral, & Jarlan, 2013;

specific humidity and precipitation have changed over land be-

Jarlan et  al.,  2008). A daily 0.05° gridded data set (1983–2017) of

tween 1983 and 2017 (Table 1; Figures 1 and 2). However, there



Temperature (°C)



Total change

0.029 (0.002)***



0.004 (0.004)


Cloud cover (%)


6.9e-3 (0.005)


Specific humidity (g/m3)


Precipitation (mm/6 hr)


0.0014 (2.5e-4)**


−2.9e-4 (5.2e-4)

Day/Night Day/Night

−6.4e-4 (0.01)


0.013 (0.0013)*** −0.0013 (0.0026)



.72 .12

0.46 0.046


0.05 −0.01

Note: Statistical significance is shown as: **0.5°C more during the daytime than the night-time expe-

cover has decreased showing a trend of greater daytime warming

rienced reduced levels of cloud cover, specific humidity and precipita-

(Figure 1).

tion in both the daytime and night-time (Table 3b; Figure 3b).

Over twice the area of land has experienced increased warming

At smaller spatial scales, the Tibetan Plateau has experienced

at night of >0.25°C relative to the daytime, whereas similar areas

median night-time minimum temperature increases of 1.2°C

of land saw changes in cloud cover of >2.5% across the daytime or

(range: 0.21°C; 3.44°C) more than daytime maximum tempera-

the night-time (Table  2; Figures  1 and 2). Globally, over twice the

tures, and this has been accompanied by increases in daytime

area of land has experienced increased specific humidity >0.1 g/m3

and even greater increases in night-time cloud cover (Table  3c;

during the daytime than the night-time (Table 2; Figure 1), whilst in-

Figure 4a). We found that specific humidity increased more in the

creased rainfall of >0.1 mm/6 hr during the daytime has been more

daytime than the night-time, and there was some evidence that

common than during the night-time. Although statistically signif-

night-time precipitation has increased more quickly than daytime

icant, temperature change difference was less strongly correlated

precipitation (Table 3c; Figure 4a). In West Africa there has been

with cloud cover change difference and precipitation change dif-

a median temperature change difference of night-time minimum

ference (Pearson's correlation 0.25°C in night-time minimum temperatures

changing climate. We examined one possible biological response to



COX et al.

warming asymmetry, in the relationship between precipitation and

Wu, 2006; Hua et al., 2018). The Plateau has also experienced diel

vegetation growth. Night-time warming was associated with a wet-

asymmetry in the other climate variables, with increased cloud cover

ting of the climate. Thus, in regions where there was a greater in-

and some evidence of increased specific humidity at night, whilst

crease in precipitation, the associated increase in cloud cover and

precipitation is greater during the daytime. The Plateau has been

reduction in daytime maximum temperatures and available sunlight

the focus of research on the ecological impacts of warming asym-

for photosynthesis is likely to have driven the negative associa-

metry, primarily related to plant growth, and these impacts have

tion between rainfall and vegetation growth. Conversely, daytime

been found to be diverse, from increased vegetation growth (Xia

warming was associated with a drying of the climate, and so here it

et al., 2018), shifting phenology (Liu, Yin, Shao, & Qin, 2006; Meng

is likely that water availability is the constraining factor, resulting in

et al., 2019; Suonan, Classen, Zhang, & He, 2017), to reduced crop

a positive relationship between vegetation growth and rainfall. The

yields (Peng et al., 2004), soil respiration (Zhong, Zhang, & Zhang,

inherent asymmetry in change complicates predictions of ecological

2019) and nectar yields (Mu et al., 2015). However, ecological stud-

responses, especially in complex systems. For example, global trends

ies have yet to consider the impacts of diel asymmetry in other as-

in LAI are also strongly influenced by other factors, such as land use

pects of the changing climate.

change (Hardwick et  al.,  2015) and local levels of CO2 (Dermody,

The biological impacts of diel asymmetry on climate are not solely

Long, & DeLucia, 2006; Li et al., 2018). However, their inclusion was

dependent on the magnitude of change, but also on the latitude at

outside of the scope of this study though likely accounts for the low

which change is taking place. In tropical regions, for example, spe-

explanatory power of the model (pR 2 = .03).

cies have evolved under relatively constant and stable climatic con-

Warming asymmetry has also been found to impact the be-

ditions (Janzen, 1967; Sheldon, Huey, Kaspari, & Sanders, 2018), and

haviour and physiology of ectotherms leading to impacts on individ-

live closer to their thermal optimum. As such, even a small tempera-

ual fitness, species interactions and ecosystem processes (Barton

ture increase can have a large impact on their physiology, behaviour

& Schmitz,  2018; Speights & Barton,  2019). Regardless of whether

and population sizes (Deutsch et al., 2008). Projections suggest that

faster warming has occurred during the daytime or night-time, re-

even with 0.25°C, and we provide further support that this is driven

>3°C between night-time minimum and daytime maximum tempera-

primarily by changing levels of cloud cover and is associated with a

tures (Table 3c; Figure 4a). The Plateau has an average elevation of

wetting (increased night-time warming) and drying (increased daytime

4,500  m, being the highest and largest highland in the world and

warming) of the climate. Levels of night-time warming were similar

rates of warming and cloud cover have been found to be significantly

regardless of whether there was greater daytime or night-time warm-

amplified by elevation, particularly during the night-time (Duan &

ing, indicating that increased night-time warming is largely driven by



COX et al.

increased cloud cover damping daytime temperatures as opposed to raising night-time temperatures (see also Dai et al., 1999). Increased night-time warming was more prevalent in wetter regions, where increased cloud cover reduces photosynthesis and drives a negative correlation between precipitation and vegetation growth. Increased daytime warming was associated with drier regions, where water availability may be the limiting factor on vegetation growth and results in a positive correlation between precipitation and vegetation growth. Diel asymmetry in how the climate is changing is likely to have major implications for temperature and water-dependent ecological processes, impacting species through thermoregulatory and water budgets. The broader implications for ectotherms and endotherms are likely to be profound. By considering climate change spatially and temporally, over the diel cycle, we can more accurately assess the climate threat posed to species, especially for those that show distinct time partitioning in biologically meaningful activities and processes. AC K N OW L E D G E M E N T S K.J.G. was supported by a Natural Environment Research Council (NERC) grant NE/P01156X/1. A.S.G. was funded by an NERC grant NE/P01229/1 with support from the Cornwall Council. C O N FL I C T O F I N T E R E S T The authors declare no conflict of interest. AU T H O R C O N T R I B U T I O N D.T.C.C., I.M.D.M. and K.J.G. conceived and designed the study. I.M.D.M. generated the hourly climate data and the LAI layer. D.T.C.C., I.M.D.M. and A.S.G. analysed the data. D.T.C.C. and A.S.G. wrote the paper. All authors contributed critically to the drafts and gave final approval for publication of the paper. This research has not been previously presented elsewhere. DATA AVA I L A B I L I T Y S TAT E M E N T Data derived from public domain resources. The NCEP_Reanalysis 2 data used in this study are available from NOAA/OAR/ESRL PSD, Boulder, Colorado, United States, from their website https://www. esrl.noaa.gov/psd/. Leaf area index values are available from the National Climatic Data Center https://www.ncei.noaa.gov/data/ avhrr​-land-leaf-area-index​-and-fapar/​acces​s/. ORCID Daniel T. C. Cox 

https://orcid.org/0000-0002-3856-3998 https://orcid.org/0000-0001-8030-9136

Ilya M. D. Maclean  Alexandra S. Gardner  Kevin J. Gaston 



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S U P P O R T I N G I N FO R M AT I O N Additional supporting information may be found online in the Supporting Information section.

How to cite this article: Cox DTC, Maclean IMD, Gardner AS, Gaston KJ. Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index. Glob Change Biol. 2020;00:1–13. https://doi.org/10.1111/gcb.15336

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| Received: 13 April 2020    Accepted: 11 August 2020 DOI: 10.1111/gcb.15336 PRIMARY RESEARCH ARTICLES Global variation in diurnal asymmetry in tem...
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